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In this paper, we propose an ordinal optimization (OO) based algorithm for solving the resource allocation optimization problem of grid computing system to maximize the service reliability. An approximate model is firstly proposed to estimate the service reliability of a resource allocation design within a tolerable computation time. Next, we employ the proposed algorithm to solve the resource allocation...
In this paper, a fuzzy facility location model with Value at Risk (VaR) is proposed, which is a two-stage fuzzy zero-one integer programming. Since the fuzzy parameters of the location problem are continuous fuzzy variables with an infinite support, the computation of VaR is inherently an infinite-dimensional optimization problem, which can not be solved analytically. In order to solve the model,...
Multi-objective evolutionary algorithms (MOEA) are particularly suitable to solve real life problems, but they have some limitations when dealing with problems with many objectives, typically more than three. Recently, some many-objective techniques were proposed to avoid the deterioration of the search ability of Pareto dominance based MOEA for many-objective problems. This work applies the control...
A fractional order PID (FOPID) controller to damp inter area oscillations of power systems is studied. FOPID is a PID whose derivative and integral orders are fractional rather than integer. The difficulty of design a FOPID controller is how to determine the five key parameters of FOPID. This paper uses genetic algorithm particle swarm optimization (GAPSO) algorithm to design the FOPID controller...
Heuristic search methods usually require a large amount of evolutionary iterative calculation, which has become a bottleneck for applying them to practical engineering problems. In order to reduce the number of analysis of heuristic search methods, a Pareto multi-objective particle swarm optimization (MOPSO) method is presented. In this approach, Pareto fitness function is used to select global extremum...
This paper presents a new class of two-stage fuzzy location-allocation problems with credibility objective, in which the customer demands are uncertain and assumed to be characterized by fuzzy variables. Since the fuzzy demand usually has an infinite support, we cannot solve the location-allocation problem by conventional optimization algorithms. To overcome this difficulty, we apply an approximation...
In this paper, a new approach for function approximation is proposed to obtain better approximated performance. It is well known that gradient-based learning algorithms such as backpropagation (BP) algorithm have good ability of local search, whereas particle swarm optimization (PSO) has good ability of global search. Therefore, in the new approach, adaptive PSO (APSO) is applied to train network...
A novel hybrid evolutionary system HPSONN combing an improved particle swarm optimization using multiple swarms(MCPSO) and a binary particle swarm optimization (BPSO) is proposed for joint optimization of three-layer feed-forward artificial neural networks (ANNs). In the proposed method, the topology of neural network is optimized by BPSO and connection weights are training by MCPSO. The experiment...
In order to make the classification rules more reliable in dealing with the decision data of evaluation, a rough rule mining approach is proposed. With uncertain measurement of rough set, lower approximation reduction based on variable precision rough set (VPRS) is analyzed. The definition of measure in decision table is given based on information entropy measure. Then based on discrete particle swarm...
This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different leader selection schemes in a multi-objective particle swarm optimizer (MOPSO), in order to determine the most appropriate approach to be adopted for solving the sort of problems of our...
Multi-objective meta-heuristics permit to conceive a complete novel approach to induce classifiers, where the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. Furthermore, these rules can be used as an unordered classifier, in this way, the rules are more intuitive and easier to understand...
Based on equilibrium chance theory, this paper presents a new class of fuzzy random minimum risk portfolio selection problem. In this problem, values of some functions are numerical characteristics of fuzzy random phenomena dependent on decision variables. This feature leads to the main difficulty encountered in solving the proposed portfolio selection problem. Therefore, conventional solution methods...
This paper proposes an approach to the solution of multi-objective optimisation problems that delivers a single, preferred solution. A conventional, population-based, multi-objective optimisation method is used to provide a set of solutions approximating the Pareto front. As the set of solutions evolves, an approximation to the Pareto front is derived using a Kriging method. This approximate surface...
In this paper, we consider a multiperiod fuzzy production and sourcing problem that a manufacturer has a number of plants and/or subcontractors. The manufacturer has to meet the products demand according to the service level requirements set by its customers. Based on credibility theory, a new class of fuzzy production planning model is first proposed. Then we deal with the approximation of the fuzzy...
Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a suitable...
Backpropagation learning algorithm for multilayer perceptrons (MLPs) has disadvantages of slow convergence and easily being trapped into local optimum. Inspired by efficient global searching ability of particle swarm optimization (PSO), a novel PSO based backpropagation learning algorithm (PSO-BP) is proposed. At first, training procedure for MLPs is formulated as nonlinear optimization problem that...
Particle swarm optimization (PSO) is a population-based stochastic recursion procedure, which simulates the social behavior of a swarm of ants or a school of fish. Based upon the general representation of individual particles, this paper introduces a decreasing coefficient to the updating principle, so that PSO can be viewed as a regular stochastic approximation algorithm. To improve exploration ability,...
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